Zobrazeno 1 - 10
of 8 779
pro vyhledávání: '"Manjunath, P"'
In this work, we propose a method for domain-incremental learning for audio classification from a sequence of datasets recorded in different acoustic conditions. Fine-tuning a model on a sequence of evolving domains or datasets leads to forgetting of
Externí odkaz:
http://arxiv.org/abs/2412.17424
We prove log-concavity of the lengths of the top rows of Young diagrams under Poissonized Plancherel measure. This is the first known positive result towards a conjecture of Chen that the length of the top row of a Young diagram under the Plancherel
Externí odkaz:
http://arxiv.org/abs/2412.15116
A novel point cloud diffusion model for relativistic heavy-ion collisions, capable of ultra-fast generation of event-by-event collision output, is introduced. When trained on UrQMD cascade simulations, the model generates realistic collision event ou
Externí odkaz:
http://arxiv.org/abs/2412.10352
We consider the following Colonel Blotto game between parties $P_1$ and $P_A.$ $P_1$ deploys a non negative number of troops across $J$ battlefields, while $P_A$ chooses $K,$ $K < J,$ battlefields to remove all of $P_1$'s troops from the chosen battl
Externí odkaz:
http://arxiv.org/abs/2412.06222
Autor:
Sahadevan, Vijayalaxmi, Mario, Sushil, Jaiswal, Yash, Bajpai, Divyanshu, Singh, Vishal, Aggarwal, Hiralal, Suresh, Suhas, Maigur, Manjunath
Ontology-based knowledge graphs (KG) are desirable for effective knowledge management and reuse in various decision making scenarios, including design. Creating and populating extensive KG based on specific ontological models can be highly labour and
Externí odkaz:
http://arxiv.org/abs/2412.05868
Autor:
Vayani, Ashmal, Dissanayake, Dinura, Watawana, Hasindri, Ahsan, Noor, Sasikumar, Nevasini, Thawakar, Omkar, Ademtew, Henok Biadglign, Hmaiti, Yahya, Kumar, Amandeep, Kuckreja, Kartik, Maslych, Mykola, Ghallabi, Wafa Al, Mihaylov, Mihail, Qin, Chao, Shaker, Abdelrahman M, Zhang, Mike, Ihsani, Mahardika Krisna, Esplana, Amiel, Gokani, Monil, Mirkin, Shachar, Singh, Harsh, Srivastava, Ashay, Hamerlik, Endre, Izzati, Fathinah Asma, Maani, Fadillah Adamsyah, Cavada, Sebastian, Chim, Jenny, Gupta, Rohit, Manjunath, Sanjay, Zhumakhanova, Kamila, Rabevohitra, Feno Heriniaina, Amirudin, Azril, Ridzuan, Muhammad, Kareem, Daniya, More, Ketan, Li, Kunyang, Shakya, Pramesh, Saad, Muhammad, Ghasemaghaei, Amirpouya, Djanibekov, Amirbek, Azizov, Dilshod, Jankovic, Branislava, Bhatia, Naman, Cabrera, Alvaro, Obando-Ceron, Johan, Otieno, Olympiah, Farestam, Fabian, Rabbani, Muztoba, Baliah, Sanoojan, Sanjeev, Santosh, Shtanchaev, Abduragim, Fatima, Maheen, Nguyen, Thao, Kareem, Amrin, Aremu, Toluwani, Xavier, Nathan, Bhatkal, Amit, Toyin, Hawau, Chadha, Aman, Cholakkal, Hisham, Anwer, Rao Muhammad, Felsberg, Michael, Laaksonen, Jorma, Solorio, Thamar, Choudhury, Monojit, Laptev, Ivan, Shah, Mubarak, Khan, Salman, Khan, Fahad
Existing Large Multimodal Models (LMMs) generally focus on only a few regions and languages. As LMMs continue to improve, it is increasingly important to ensure they understand cultural contexts, respect local sensitivities, and support low-resource
Externí odkaz:
http://arxiv.org/abs/2411.16508
Complementable operators extend classical matrix decompositions, such as the Schur complement, to the setting of infinite-dimensional Hilbert spaces, thereby broadening their applicability in various mathematical and physical contexts. This paper foc
Externí odkaz:
http://arxiv.org/abs/2411.15636
We analyze the effect that online algorithms have on the environment that they are learning. As a motivation, consider recommendation systems that use online algorithms to learn optimal product recommendations based on user and product attributes. It
Externí odkaz:
http://arxiv.org/abs/2411.13883
This paper investigates the feasibility of class-incremental learning (CIL) for Sound Event Localization and Detection (SELD) tasks. The method features an incremental learner that can learn new sound classes independently while preserving knowledge
Externí odkaz:
http://arxiv.org/abs/2411.12830
Autor:
Krishnapur, Manjunath, Yogeshwaran, D.
We consider covariance asymptotics for linear statistics of general stationary random measures in terms of their truncated pair correlation measure. We give exact infinite series-expansion formulas for covariance of smooth statistics of random measur
Externí odkaz:
http://arxiv.org/abs/2411.08848